Designing search and recommendation models that are both efficient and effective has long been a central objective for both industry professionals and academic researchers. Yet, growing evidence highlights how models trained on historical data can reinforce pre-existing biases, potentially leading to harmful outcomes. Addressing these challenges by defining, evaluating, and mitigating bias across development workflows is a crucial step toward the responsible deployment of search and recommendation models in practice. The BIAS 2025 workshop seeks to gather innovative research and foster a shared space for dialogue among researchers and practitioners committed to advancing this fundamental direction. Workshop website: https://biasinrecsys.github.io/sigir2025/.

Creating efficient and effective search and recommendation algorithms has been the main objective of industry practitioners and academic researchers over the years. However, recent research has shown how these algorithms trained on historical data lead to models that might exacerbate existing biases and generate potentially negative outcomes. Defining, assessing, and mitigating these biases throughout experimental pipelines is a primary step for devising search and recommendation algorithms that can be responsibly deployed in real-world applications. This workshop aims to collect novel contributions in this field and offer a common ground for interested researchers and practitioners. More information about the workshop is available at https://biasinrecsys.github.io/sigir2024/

Abstract Bias in news search engines has been shown to influence users' perceptions of a news topic and contribute to the polarisation of society. As a result, there is a need for news search engines that increase user awareness of biases in the search results. While technical approaches have been developed to mitigate biases in search, very few studies have investigated user preferences in interface designs for potentially raising their awareness of biases in news search engines. In this study, we utilized a participatory design methodology to develop eight prototypes with different features that could potentially be used to raise user awareness of biases in news search engines. We conducted three user studies, involving 132 participants with Computer Science backgrounds, to evaluate these prototypes. Our findings indicate the importance of news search engines that (a) inform users of possible biases in the results (bias visualization approach) and (b) allow users to access alternative search results (results‐reranking approach). Our study provides further insights into the strengths and possible risks of each approach, which are important for future research on designing interfaces for raising user awareness of biases in news search engines.

Crowdsourcing plays a key role in developing algorithms for image recognition or captioning. Major datasets, such as MS COCO or Flickr30K, have been built by eliciting natural language descriptions of images from workers. Yet such elicitation tasks are susceptible to human biases, including stereotyping people depicted in images. Given the growing concerns surrounding discrimination in algorithms, as well as in the data used to train them, it is necessary to take a critical look at this practice. We conduct experiments at Figure Eight using a controlled set of people images. Men and women of various races are positioned in the same manner, wearing a grey t-shirt. We prompt workers for 10 descriptive labels, and consider them using the human-centric approach, which assumes reporting bias. We find that “what’s worth saying” about these uniform images often differs as a function of the gender and race of the depicted person, violating the notion of group fairness. Although this diversity in natural language people descriptions is expected and often beneficial, it could result in automated disparate impact if not managed properly.

There is an establishing trend towards the socialization of the web. Virtual communities are becoming very popular web spaces for collaboration and knowledge sharing. However, studies have shown that virtual communities may often be ineffective and may not sustain. We propose a novel approach for community-tailored support which is aimed at facilitating processes important for the community’s effectiveness and sustainability. The paper presents algorithms for detecting evolution patterns of community knowledge sharing behavior. The algorithms are applied to a model of an existing closely-knit community, and used to identify when and what intelligent interventions may be needed to support the functioning of the community as an entity.